ANNIDS: Intrusion detection system based on artificial neural network

被引：7
|
作者：
Liu, YH [1 ]
Tian, DX [1 ]
Wang, AM [1 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China
来源：
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词：
intrusion detection system; neural network; packet; hamming distance;
D O I：
10.1109/ICMLC.2003.1259699
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper describes a network intrusion detection system based on artificial neural network (ANNIDS). The advantage of neural network ensures that ANNIDS does not need expert knowledge and it can find unknown or novel intrusions. The key part of ANNIDS is an adaptive resonance theory neural network (ART). ANNIDS can be trained in real-time and in an unsupervised way. A weight hamming distance method is used in detection, which is simple and correct in finding anomalous behavior. A well-trained ANNIDS can monitor the network in real time. The experimental results show that ANNIDS performs best when vigilance parameter is 0.4 to 0.5 and intrusion threshold is 0.4. The false positive error is about 8%, the negative error is about 2%, and the total error is lower 10%.
引用
收藏
页码：1337 / 1342
页数：6
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